WELDING JUDGMENT DEVICE, WELDING JUDGMENT DEVICE SYSTEM, WELDING JUDGMENT METHOD, AND WELDING JUDGMENT PROGRAM

Abstract

To provide a welding determination device capable of reducing the management burden of welding conditions while increasing the types of welding conditions, as well as, capable of enhancing the accuracy of welding management.SOLUTION: A welding determination device comprises: a target information acquisition unit 110 for acquiring welding target information of a welding object itself; a welding condition accumulation unit 120 for accumulating accumulated information including a plurality of types of welding conditions controlled by a welding device at welding of the welding object and appropriateness of welding quality associated respectively with each other; an appropriate information generation unit 130 for generating and accumulating, as appropriate information, combinations of the appropriateness of welding quality and welding conditions corresponding to the associated welding target information; a sequential acquisition unit 140 for acquiring, as sequential welding condition information, welding conditions of the same types as a plurality of types of welding conditions controlled by the welding device at welding of the welding object; a collation unit 150 for collating the sequential welding condition information with the appropriate information; a determination unit 160 for determining whether or not the sequential welding condition information deviates from the appropriate information and generates a determination result; and an output unit 170 for outputting the determination result.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to a welding judgment device, a welding judgment device system, a welding judgment method, and a welding judgment program, and in particular to a welding judgment device and welding judgment device system, as well as a welding judgment method and a welding judgment program, that accumulate welding conditions detected from a welding device and can judge the suitability of welding from fluctuations in the welding conditions. [Background technology]

[0002] In the field of welding machines, due to the trend toward automating the monitoring of welding conditions and the determination of welding states, there are welding devices (robots) and systems equipped with machine learning and artificial intelligence (AI) technology. As examples of such devices, various documents have been disclosed, such as a spot welding device that determines the welding state (Patent Document 1), a welding monitoring system (Patent Document 2), and a spot welding quality diagnosis system (Patent Document 3), and a welding monitoring system and welding monitoring method for a resistance welding machine (Patent Document 4).

[0003] However, in the previously disclosed welding devices and systems, the content and types of data showing the state during welding are few, and the amount of data accumulated in the welding device is limited. Therefore, when a welding defect occurs during welding, the accumulated data is insufficient as a basis for determining the cause of the defect. Therefore, when a welding defect occurs, the cause cannot be immediately clarified, and it takes time to determine the cause and take measures such as changing the welding state. As a result, the equipment operating rate and productivity at the manufacturing site are reduced.

[0004] Furthermore, in the case of welding automobile bodies, in addition to the fact that the types of steel plates have increased, aluminum plates and the like have also been added to the list of body materials to be welded. As the types of objects to be welded have increased, the welding conditions have become more complex. Specifically, welding conditions such as multi-stage current application, multi-stage pressure, upslope and downslope in spot welding are used. This has increased the burden of managing the welding conditions to suit the intended objects to be welded. Due to this increased burden, the manufacturing site has had to shoulder an increased burden of allocating a large number of personnel when setting up new manufacturing equipment and maintaining manufacturing equipment that is currently in operation. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2018-034172 A [Patent Document 2] JP 2018-001184 A [Patent Document 3] JP 2016-203246 A [Patent Document 4] JP 2020-179406 A Summary of the Invention [Problem to be solved by the invention]

[0006] In light of this series of events, the inventors have earnestly investigated methods for increasing the types of welding conditions to be managed, thereby improving the accuracy of welding management, and reducing the burden of managing the welding conditions, in order to respond to the diversification of welding objects and welding conditions.

[0007] The present invention has been made in consideration of the above points, and provides a welding judgment device, a welding judgment device system, a welding judgment method, and a welding judgment program that can reduce the burden of managing welding conditions while increasing the number of welding conditions, and at the same time improve the accuracy of welding management. [Means for solving the problem]

[0008] That is, the welding judgment device judges the welding state of a welding equipment that welds workpieces, and the welding judgment device is characterized by comprising: an object information acquisition unit that acquires welding object information related to the workpieces themselves; a welding condition storage unit that accumulates accumulated information correlating the appropriateness of welding quality with each of multiple types of welding conditions controlled by the welding equipment when welding the workpieces in accordance with the welding object information; an appropriateness information generation unit that generates and accumulates, as appropriate information, an association of welding conditions corresponding to the welding object information that is associated with the appropriateness of welding quality by a statistical method using the accumulated information; a sequential acquisition unit that acquires, as sequential welding condition information, welding conditions of the same type as the multiple types of welding conditions controlled by the welding equipment when welding the workpieces corresponding to the welding object information; a comparison unit that compares the sequential welding condition information with the appropriateness information; a judgment unit that judges whether the sequential welding condition information deviates from the appropriateness information and generates a judgment result; and an output unit that outputs the judgment result.

[0009] Furthermore, the welding condition storage unit of the welding judgment device may acquire a plurality of types of welding conditions controlled by the welding device for each welding of the workpiece.

[0010] Furthermore, the statistical method may be machine learning using teacher data in which the welding quality when welding the workpieces is used as teacher data and multiple types of welding conditions are used as input data.

[0011] Furthermore, the statistical method may be a multivariate analysis in which the welding quality is the objective variable and a plurality of types of welding conditions are the explanatory variables.

[0012] Furthermore, the welding judgment device may be provided with a display unit that displays a time series graph of welding conditions corresponding to each of the multiple types of welding conditions included in the suitability information, and a predetermined precision may be set for the fluctuation range of the time series graph.

[0013] Furthermore, the welding judgment device may be provided with a display unit that displays a time-series graph of welding conditions corresponding to each of the multiple types of welding conditions included in the suitability information, and a threshold value may be set for the fluctuation range of the time-series graph.

[0014] Furthermore, the determination unit may generate the determination result by using a determination formula obtained by a statistical method using the determination result and the suitability information.

[0015] Furthermore, the judgment unit of the welding judgment device may be configured to judge whether or not at least one of the plurality of welding conditions included in the sequential welding condition information deviates from the appropriate information.

[0016] Furthermore, the welding device may be a resistance welder.

[0017] Furthermore, the welding judgment device may be implemented in each of the welding devices. Effect of the Invention

[0018] The welding judgment device of the present invention includes an object information acquisition unit that acquires welding object information related to the workpiece itself, a welding condition storage unit that stores accumulated information correlating the appropriateness of welding quality with each of multiple types of welding conditions controlled by the welding equipment when welding the workpieces in accordance with the welding object information, an appropriateness information generation unit that generates and stores, as appropriate information, an association of welding conditions corresponding to the welding object information associated with the appropriateness of welding quality by a statistical method using the accumulated information, a sequential acquisition unit that acquires, as sequential welding condition information, welding conditions of the same type as the multiple types of welding conditions controlled by the welding equipment when welding the workpieces corresponding to the welding object information, a comparison unit that compares the sequential welding condition information with the appropriateness information, a judgment unit that judges whether the sequential welding condition information deviates from the appropriateness information and generates a judgment result, and an output unit that outputs the judgment result.Therefore, the burden of managing the welding conditions can be reduced while increasing the number of welding conditions, and the accuracy of welding management can be improved. [Brief description of the drawings]

[0019] [Figure 1] 1 is a schematic diagram showing a configuration of a welding judgment system including a welding judgment device according to an embodiment; [Diagram 2] 1 is a schematic block diagram showing a configuration of a welding judgment device; [Diagram 3] FIG. 2 is a schematic block diagram showing a functional section of a welding judgment device. [Figure 4] FIG. 1 is a schematic diagram showing the main parts of a resistance welding machine. [Diagram 5] FIG. 2 is a schematic cross-sectional view showing a main portion of a resistance welded portion. [Figure 6] FIG. 2 is a schematic diagram showing an example of welding conditions for a resistance welding machine. [Figure 7] FIG. 1 is a schematic diagram showing a feedback flow of a previous weld defect. [Figure 8] FIG. 2 is a schematic diagram showing an overview of a process in a welding judgment device. [Figure 9] FIG. 2 is a first exemplary schematic diagram showing an example of a process in the welding judgment device. [Figure 10] FIG. 11 is a second schematic diagram showing an example of a process in the welding judgment device. [Figure 11] 4 is a first flowchart showing a processing procedure in the welding judgment device of the embodiment. [Figure 12] 5 is a second flowchart showing the processing procedure in the welding judgment device of the embodiment. [Figure 13] 10 is a third flowchart showing the processing procedure in the welding judgment device of the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] The schematic diagram of Fig. 1 shows a welding judgment system 1 including a plurality of welding devices 2, 3, and 4, each of which is connected to a welding judgment device 100. The welding device illustrated as an embodiment is a welding device called a resistance welding machine or a spot welding machine. In the description of the embodiment, the welding device will be described using a resistance welding machine as an example.

[0021] The welding judgment device 100 is installed in each of the multiple welding devices 2, 3, and 4, and stores and monitors various welding conditions (see FIGS. 5 and 6 described below) for the workpiece when operating the welding device (resistance welding device) required for welding, such as the current and voltage in the welding devices 2, 3, and 4 (resistance welding device). The welding judgment device 100 plays a role in judging the welding condition of the welding device, such as defective welding of the workpiece. Since the welding judgment device 100 is installed in each of the multiple welding devices 2, 3, and 4, it serves as an edge computing device implemented in each welding device.

[0022] In the welding judgment system 1, the welding judgment device 100 installed in each of the multiple welding devices 2, 3, 4 is connected to an in-line measuring device, a resistance welding controller, and a programmable logic controller (not shown), and is further connected to a monitoring device 500. A server, a display, etc. are also connected to the monitoring device 500 as appropriate. The welding judgment device 100 and the monitoring device 500 have substantially the same functions. A welding judgment device 100 is installed for each individual welding device. That is, the monitoring device 500 monitors the welding conditions of each of all of the welding devices 2, 3, 4 that are connected to it.

[0023] In the figure, the number of welding devices is three as shown. The number of welding devices is not limited to three, and can be one or increased to four or more. Of course, this configuration example is just one example, and each device can be added or omitted as necessary. The welding judgment device 100 will now be described in detail with reference to the figures.

[0024] The welding judgment device 100 installed in each of the multiple welding devices 2, 3, and 4 is a computer that monitors the welding conditions of the installed welding device and judges the suitability of welding based on the deviation from the existing accumulated welding conditions. As shown in the schematic block diagram of FIG. 2, the welding judgment device 100 is equipped with a CPU 101, a ROM 102, a RAM 103, a storage unit 104, an input unit 105, an output unit 106, etc. in terms of hardware. A main memory, an LSI, etc. are also included. The monitoring device 500 also has a similar hardware configuration to that shown in the schematic block diagram of FIG. 2. The welding judgment device 100 and the monitoring device 500 are various electronic computers (computational resources) such as personal computers (PCs), mainframes, workstations, tablet terminals, and smartphones.

[0025] The input unit 105 and the output unit 106 are known input / output interfaces, and are connected to the welding device 2 disclosed in Fig. 1 and the like. In the case of the monitoring device 500, a display (such as a liquid crystal display device or an organic EL display device) is connected to the output unit 106 as the display unit 107. These are merely examples, and may be appropriately combined and selected optimally.

[0026] The memory unit 104 of the welding judgment device 100 (monitoring device 500) is a known storage device such as an HDD or SSD. In addition, each functional unit that executes various calculations in the welding judgment device 100 (monitoring device 500) is a calculation element such as a CPU 101. As shown in the schematic block diagram of FIG. 3, each functional unit in the CPU 101 of the welding judgment device 100 (monitoring device 500) includes a target information acquisition unit 110, a welding condition accumulation unit 120, an appropriateness information generation unit 130, a sequential acquisition unit 140, a collation unit 150, a judgment unit 160, an output unit 170, a machine learning unit 180, and the like. The operation and execution of the welding judgment device 100 (monitoring device 500) is realized in a software manner by a welding monitoring program for a resistance welding machine loaded into a main memory, and the like.

[0027] When each functional unit of the welding judgment device 100 (monitoring device 500 of the welding judgment system 1) in Fig. 1 is realized by software, the welding judgment device 100 (monitoring device 500) is realized by executing instructions of a program, which is software that realizes each function. The recording medium that stores this program may be a "non-transitory tangible medium," such as a CD, a DVD, a semiconductor memory, or a programmable logic circuit. In addition, this program may be supplied to the welding judgment device 100 (monitoring device 500) via any transmission medium (communication network, broadcast wave, etc.) that can transmit the program.

[0028] The schematic diagram of FIG. 4 shows the main parts of the welding device 2. Since the welding devices 2, 3, and 4 in FIG. 1 are the same machine, the welding device 2 is shown as a representative. A resistance welding portion 11 is provided at the tip of the welding device 2 (resistance welding machine). The resistance welding portion 11 shown in the figure is a portion with an inverted C-shaped clamp structure. The shape of the resistance welding portion 11 is appropriately selected depending on the target portion of the resistance welding. Electrode portions 12 and 13 are connected to the resistance welding portion 11. The electrode portions 12 and 13 are consumables and can be attached and detached freely, and are replaced from the resistance welding portion 11 due to wear or the like. A welding judgment device 100 is also connected to the resistance welding portion 11 in order to measure the welding conditions of the resistance welding portion 11. The welding device 2 has an arm portion connected by a joint portion, and the joint portion is equipped with a servo motor (not shown).

[0029] 5, the workpieces W1 and W2 are placed between the electrode portion 12 and the electrode portion 13 of the resistance welded portion 11. Then, as the electrode portion 12 advances toward the electrode portion 13, the electrode portion 12 and the electrode portion 13 come into contact with and are crimped against the workpieces W1 and W2. Then, resistance heating occurs due to the passage of electricity from the electrode portion 12 and the electrode portion 13 of the resistance welded portion 11 to the workpieces W1 and W2, causing the metals of the workpieces W1 and W2 to partially melt, and a metal molten site 14 is formed between the workpieces W1 and W2.

[0030] As shown in the schematic diagram of FIG. 6, for example, when the welding device 2 (resistance welding machine) is of AC or DC type, the supply voltage is stepped down by a transformer (not shown). At this time, the stepped-down voltage on the secondary side of the transformer is measured through a voltage detection line wired to the resistance welding portion 11. In addition, a non-contact ammeter 22 such as a CT type ammeter is attached to the resistance welding portion 11. The thin dashed lines in FIG. 6 indicate the wiring flow of the actual voltage detection line measured when the resistance welding portion 11 is energized (during welding). The thick dashed lines in FIG. 6 indicate the wiring flow of the actual non-contact ammeter 22 measured by the non-contact ammeter 22 when the resistance welding portion 11 is energized (during welding). Since the resistance welding portion 11 is provided with a voltage detection line, a non-contact ammeter 22, etc., the welding conditions are constantly measured (constantly monitored) every time resistance welding is performed. Of course, the measurement method shown and described is just an example, and the measurement is performed appropriately according to the method of the welding device 2 (resistance welding machine).

[0031] In the example of Fig. 6, the current and voltage during operation of the welding device 2 (resistance welding machine) are exemplified as the welding conditions. Furthermore, the welding conditions include digitized information such as current flow time, the pressure applied to the workpiece in the case of a resistance welding machine, resistance value, distortion value, input heat amount, amount of water for cooling the device, the control amount of the motor that drives the resistance welding part (see Fig. 4), and air temperature during welding. The welding conditions can be a graph (waveform) that changes with time on the horizontal axis. As an example, in the case of pressure, a graph showing the change over time in pressure from the start to the end of welding can be used as the welding conditions. Various welding conditions are acquired by the welding judgment device 100.

[0032] Furthermore, in addition to the information acquired on the welding device side, information on the workpiece side (welding target information, peripheral information) is acquired by the welding judgment device 100. The welding target information is, for example, information on the shape of the workpiece, the material of the workpiece (type of steel plate, aluminum material, etc.), the name of the welding process (welding portion), the number of welding points, plate assembly, date and time, etc.

[0033] In other words, both the welding conditions acquired on the welding equipment side and the welding object information provided on the workpiece side are acquired by the welding judgment device 100 (monitoring device 500), making it easy for the welding judgment device 100 (monitoring device 500) to find the correlation between the welding conditions and the welding object information.

[0034] From now on, the individual functional parts of the welding judgment device 100 (monitoring device 500) (its CPU 101) will be described in order with reference to the above-mentioned Figs.

[0035] The object information acquisition unit 110 acquires welding object information related to the workpiece itself. As described above, the welding object information includes information such as the material of the workpiece, the process name (welding portion) during welding, the number of welding points, plate assembly, date and time, etc. That is, if the material of the workpiece, the welding portion, etc. are different, the welding conditions on the welding device side will obviously change. Therefore, information related to the workpiece itself, which is the premise of welding, is acquired in advance. The welding object information is acquired every time the workpiece is changed. The welding object information is acquired by inputting it in advance from a worker or the like according to the workpiece to be welded. As an example, the input is completed by reading a two-dimensional barcode or the like attached to each workpiece through a reader. Alternatively, the information may be inputted to the monitoring device 500 and then transmitted to each welding judgment device 100.

[0036] The welding condition storage unit 120 stores accumulated information in which the quality of welding is associated with each of a plurality of types of welding conditions controlled by the welding device when welding the workpiece in response to the welding object information. For example, the welding object information acquired by the object information acquisition unit 110 indicates that the material of the workpiece is SS400, resistance welding of the door frame part of an automobile vehicle, and 15 welding points are performed. In response to the welding object information, a plurality of types of welding conditions that can be managed and controlled by the welding device 2 (resistance welding machine) are comprehensively acquired, such as current, voltage, current application time, pressure applied to the workpiece, resistance value, distortion value, input heat amount, and the like. A collection of these plurality of types of welding conditions becomes accumulated information. The welding conditions that are the basis of the accumulated information depend on the welding object information, the type of the welding device itself, and information that can be acquired by the welding device.

[0037] A plurality of types of welding conditions controlled by the welding device for each welding of the workpiece are acquired in the welding condition storage unit 120. Since a plurality of types of welding conditions are acquired for each welding, omissions due to accidents or the like are avoided, and the reliability of the stored welding conditions is improved.

[0038] When constantly storing a plurality of types of welding conditions, if extremely minute variations are continuously stored, the storage capacity of the storage unit 104 of the welding judgment device 100 (monitoring device 500) may not be able to cope with the situation. In this case, the amount of data is compressed and the computation load is reduced by the method described below. First, a time series graph of the welding conditions is formed for each of the plurality of types of welding conditions (the resistance waveform, heat quantity waveform, and expansion quantity waveform in Figs. 9 and 10 are referenced). The time series graph may be displayed on the display of the display unit 107 or the like.

[0039] A predetermined precision is set for the fluctuation range of the time series graph. In the case of a graph with large up and down fluctuations, the measurement waveform is appropriately flattened, so that the amount of data stored in the storage unit 104 is reduced. To achieve the predetermined precision, the moving average line of the time series graph is used, and abnormal values ​​are statistically removed from the detected values.

[0040] In addition, a predetermined threshold is set for the fluctuation range of the time series graph. An appropriate threshold (range) is set in advance for each of the multiple types of welding conditions, and values ​​above and below the threshold are removed from the graph. In this way, the extreme fluctuation range is apparently eliminated from the time series graph, and the measurement waveform is appropriately flattened.

[0041] The suitability information generating unit 130 generates and stores, as suitability information, a link between the welding conditions corresponding to the welding object information associated with the welding quality by a statistical method using the accumulated information. In the suitability information generating unit 130, a link is made between the welding conditions corresponding to the welding object information acquired through the welding condition storing unit 120 and the welding quality corresponding to each welding condition. That is, for welding performed under the welding conditions corresponding to certain welding object information, the suitability (goodness or badness) of the welding quality is fed back (acquired, reflected) from the user's viewpoint or the like. Then, the suitability of the welding quality is collected according to various welding conditions. Each of such links between the welding conditions and the welding quality is generated as suitability information. In this way, the links between the welding conditions and the welding quality are generated as suitability information, and the suitability information is stored.

[0042] For example, when the material of the welding object is aluminum, the thermal conductivity is higher and the base material resistance is lower than that of iron, so the welding conditions that affect the welding are the pressure applied to the workpiece, the resistance value, etc. The appropriateness information is a plurality of types of welding conditions when good welding is performed on the workpiece. In other words, the appropriateness information is a collection of a plurality of types of welding conditions when the welding equipment operates normally and the workpiece is properly welded.

[0043] As a statistical method used to process the accumulated information, supervised machine learning is performed using the welding quality when the workpiece is welded as the training data and multiple types of welding conditions as the input data. For example, the acquired resistance waveform, heat amount waveform, expansion amount waveform, etc., from the accumulated information are compared with the acceptable (non-defective) welding quality (welding state) acquired by sampling inspection or non-destructive inspection of the actual welded workpiece. Note that inspection of the actual welded workpiece is only performed in the initial stage for generating the appropriateness information. Then, the accumulated information is associated (linked, annotated) with the welding quality. In this way, the training data is generated and machine learning with the training data is performed.

[0044] The statistical method used to process the accumulated information is described in more detail as a multivariate analysis, with the welding quality as the objective variable and multiple welding conditions as explanatory variables. Multivariate analysis is highly convenient as a statistical method, since it can derive relationships between multiple variables at once.

[0045] The suitability information generating unit 130 is provided with a machine learning unit 180 as an operating entity of a statistical method (machine learning). The machine learning unit 180 executes the above-mentioned machine learning with teacher data and multivariate analysis. In addition to the above-mentioned methods, examples of the machine learning analysis method in the machine learning unit 180 include regression analysis such as linear regression, logistic regression, and support vector machine. In this way, the suitability information generating unit 130 generates and accumulates suitability information that serves as a standard for determining whether welding is good or bad, based on multiple types of welding conditions corresponding to welding quality.

[0046] Through the process up to this point, the welding device (resistance welding machine, spot welding machine) is actually operated to weld various objects to be welded, and suitability information corresponding to the welding object information is generated and stored.

[0047] From this point on, the quality of welding of the workpieces is judged individually based on the accumulated information. The sequential acquisition unit 140 acquires, as sequential welding condition information, the same types of welding conditions as the multiple types of welding conditions controlled by the welding device when welding the workpieces corresponding to the welding object information. For example, in the case where the material of the workpieces is SS400, resistance welding is performed on the door frame part of an automobile vehicle, and the number of welding points is 15, the welding object information acquires, as sequential welding condition information, the current, voltage, current application time, pressure applied to the workpieces, resistance value, distortion value, input heat amount, etc., corresponding to the welding object information. The sequential acquisition unit 140 comprehensively acquires, as sequential welding condition information, multiple types of welding conditions that can be managed and controlled by the welding device 2 (resistance welding machine).

[0048] Collation unit 150 collates the sequential welding condition information with the suitability information. In this process, the sequential welding condition information (multiple types of welding conditions) is collated with the suitability information, which is multiple types of welding conditions when the welding device operates normally and appropriately welds the workpieces. In other words, multiple types of welding conditions common to the welding of the workpieces currently being performed and the welding of the workpieces previously performed are selected.

[0049] The judgment unit 160 judges whether the welding condition information deviates from the appropriate information and generates a judgment result. Here, the welding of the workpiece currently being performed is compared with the welding of the workpiece previously performed, and it is judged whether the welding conditions are appropriate. The judgment result is a conclusion that the welding of the workpiece currently being performed is "normal" or "abnormal". Furthermore, in generating the judgment result in the judgment unit 160, a judgment formula obtained by a statistical method using the judgment result and the appropriateness information is used. The statistical method referred to here is the establishment of a correlation such as the least squares method, and the level of the correlation coefficient or the coefficient of determination, and the judgment formula is a correlation formula such as the least squares method.

[0050] As described above, the welding conditions can be understood as a graph (waveform) that changes with time on the horizontal axis, and are acquired and stored as waveforms. In this case, deviations may be determined by comparing the waveforms of multiple types of welding conditions included in the sequential welding condition information with multiple types of welding conditions included in the suitability information. Furthermore, a threshold value may be set for each of the multiple welding conditions included in the suitability information, and deviations may be determined when an excess of the threshold value is detected.

[0051] As a practical matter, it is impossible for the multiple welding conditions included in the suitability information to match the multiple welding conditions included in the sequential welding condition information in terms of all values ​​and waveforms. Fluctuations may occur due to measurement errors and other factors. Therefore, as a practical solution, a threshold is set, and if the results converge within the threshold, it is possible to process the results as being OK for welding. The threshold may be set on both the upper and lower sides of the graph of the time series of welding conditions, or on either the upper or lower side.

[0052] Furthermore, the determination unit determines whether or not at least one of the plurality of welding conditions included in the sequential welding condition information deviates from any of the plurality of welding conditions included in the suitability information. Both the suitability information and the sequential welding condition information include a plurality of welding conditions. For example, when there are three types of welding conditions, if one of them deviates (exceeds a threshold value), it will be determined that there is a welding abnormality even if there is no problem with the other two types of welding conditions.

[0053] The output unit 170 outputs the judgment result. Specifically, regarding the conclusion that the welding of the workpiece currently being welded is "normal" or "abnormal", whether the welding is normal or abnormal is displayed on the display (not shown) of the display unit 107. Note that if the judgment result is abnormal, the product cannot be supplied to the production line with poor welding, and therefore countermeasures such as stopping the production line and welding again are carried out.

[0054] As can be seen from the series of processes, in the welding judgment device 100 (monitoring device 500), the welding of the workpiece currently being performed is monitored and accumulated through multiple welding conditions during that welding, and is further compared and judged with suitability information generated through machine learning based on statistical methods. In particular, it becomes possible to comprehensively monitor and judge multiple welding conditions. This makes it possible to move away from the selection of welding conditions that are dependent on the experience of on-site workers, etc. In addition, because of constant monitoring, the burden of inspections to confirm the strength of the weld, such as sampling after welding and hitting the welded area with a hammer, is greatly reduced.

[0055] The use of the welding judgment device 100 (monitoring device 500) of the embodiment will be described with reference to the schematic diagram of resistance welding in FIG. 7. The schematic diagram of FIG. 7 explains the actual situation before the introduction of the welding judgment device 100 (monitoring device 500). Normally, in resistance welding, a molten metal portion 14 is generated between the workpieces W1 and W2 (see FIGS. 5 and 7). The molten metal portion 14 is a circular portion called a nugget. Here, if the molten metal portion 14 (nugget) does not meet the specified diameter, the area of ​​the molten metal portion 14 is insufficient, resulting in insufficient welding strength and poor welding.

[0056] In this case, in consideration of the result of poor welding, a plurality of welding conditions that can be monitored by the welding device 2, etc., are acquired as a cause analysis, along with the material, shape, etc. of the workpiece. In the figure, a normal waveform (statistical waveform) and an abnormal waveform are presented. In addition, the temperature, humidity, date, time, etc. during welding are also acquired.

[0057] Then, from among the multiple welding conditions presented in the cause analysis, the welding conditions are selected based on the worker's manual power and the worker's rule of thumb, and are fed back as the welding conditions to be managed. The worker's rule of thumb takes into account disturbances, changes in the conditions of the workpiece or equipment, distortion of the workpiece itself, abnormalities in the equipment itself, and the like. Therefore, feedback for improving the welding conditions is given only after a welding defect occurs, so the responsiveness of the work is not satisfactory. In addition, even if the worker is experienced, it is difficult for the workers to share the welding conditions to be managed among themselves, and it is also not easy to convey them to workers with little experience.

[0058] Therefore, by introducing the welding judgment device 100 (welding judgment system 1 equipped with the monitoring device 500), welding object information and multiple welding conditions during normal welding are constantly acquired, and suitability information serving as a sample of a non-defective product is generated in advance, making it easy to compare and judge with newly performed welding. Furthermore, since the suitability information is generated through machine learning in the welding judgment device 100, the variation in multiple welding conditions to be adopted by each worker is eliminated without the involvement of the worker. In particular, since judgment is possible at the stage of newly performed welding, a quick response is possible even if a welding defect occurs.

[0059] FIG. 8 is a schematic diagram showing an overview of the process when welding judgment device 100 (welding judgment system 1 including monitoring device 500) is introduced. As described above, welding determination device 100 (monitoring device 500) measures and acquires welding object information and a plurality of corresponding welding conditions (S1). In this embodiment, since the welding machine is a resistance welding machine, multiple welding conditions corresponding to the welding object information are saved and accumulated for each welding point (S2). Normal welding conditions are learned as training data for machine learning from a plurality of welding conditions accumulated over a predetermined period of time (S3). Suitability information is generated by linking the welding object information with a plurality of corresponding welding conditions (S4). The suitability information is then used for judgment. Since the optimum information is generated through machine learning by associating the welding object information with a number of corresponding welding conditions, the optimum information once generated can be utilized even when adopting new welding points or plate combinations. This eliminates the need to redo everything, and makes it possible to reduce expenses required for production preparation, such as setting up new welding equipment (S5).

[0060] Fig. 9 is a schematic diagram showing an overview when welding judgment device 100 (welding judgment system 1 equipped with monitoring device 500) is introduced. Fig. 9 shows a flow from the generation of the above-mentioned suitability information to the output of suitable welding conditions.

[0061] First, multiple welding conditions are obtained along with the acquisition of welding object information. In the figure, the resistance waveform, heat amount waveform, and expansion amount waveform of the welding device 2 (resistance welding machine) are shown as welding conditions. Next, each welding condition is summarized with the current flow time on the horizontal axis and various values ​​on the vertical axis, and waveform analysis is performed using the statistical method described below. This process corresponds to data editing, or so-called visualization. Then, with the current flow time on the horizontal axis and various values ​​on the vertical axis, optimal welding conditions (judgment logic) corresponding to the welding object information are generated through machine learning as appropriate information (appropriate conditions). Since the process from the acquisition of multiple welding conditions to the generation of appropriate information is performed automatically, labor saving is achieved. If the appropriate information were to be generated manually, the number of repetitions would increase and the analysis burden would be large, making it impossible to realize.

[0062] Fig. 10 is a schematic diagram showing an overview of the introduction of welding judgment device 100 (welding judgment system 1 equipped with monitoring device 500). Fig. 10 shows a flow from the generation of the above-mentioned suitability information to the actual execution of judgment.

[0063] First, a plurality of welding conditions are acquired together with the acquisition of welding object information. In the figure, the resistance waveform, heat quantity waveform, and expansion quantity waveform of the welding device 2 (resistance welding machine) are shown as welding conditions. Next, each welding condition is summarized with the horizontal axis representing the current flow time and the vertical axis representing various values, and waveform analysis is performed using the statistical method described below. This process corresponds to data editing, or so-called visualization. Then, for example, as a plurality of welding conditions, a comparison is made between graphs of the current flow time and resistance value of each of the normal waveform and the abnormal waveform, and a comparison is made between graphs of the current flow time and expansion quantity of each of the normal waveform and the abnormal waveform. This corresponds to the processing in the collation unit 150, the determination unit 160, etc. described above. Then, in the case of abnormality detection, the abnormality determination result is output by the output unit 170.

[0064] In the waveform analysis by the statistical method in the explanation of Figures 9 and 10, a judgment formula obtained by the statistical method is used. The statistical method referred to here is the establishment of a correlation such as the least squares method, and the magnitude of the correlation coefficient or the coefficient of determination, and the judgment formula is a correlation formula such as the least squares method. The welding conditions can be grasped as a graph (waveform) that changes with the horizontal axis being time, and are acquired and stored as waveforms. In this case, deviations may be judged by comparing the waveforms of multiple types of welding conditions. Furthermore, a threshold value may be set for each of the multiple welding conditions included in the suitability information, and when an excess of the threshold value is detected, it may be judged as a deviation.

[0065] It is impossible for multiple types of welding conditions to match in all of their values ​​and waveforms. Fluctuations may occur due to measurement errors and other factors. In reality, therefore, a threshold is set, and if the results converge within the threshold, they can be treated as normal. The threshold may be set on both the upper and lower sides of the graph of the time series of welding conditions, or on either the upper or lower side.

[0066] Hereinafter, a welding judgment method and a welding judgment program according to an embodiment will be described with reference to the flowcharts of Fig. 11 and Fig. 12. The welding judgment method is executed by CPU 101 (computer) of welding judgment device 100 (monitoring device 500) based on the welding judgment program of welding judgment device 100 (monitoring device 500). The welding judgment program causes CPU 101 (computer) in Fig. 2 and Fig. 3 to execute a target information acquisition function, a welding condition accumulation function, an appropriate information generation function, a sequential acquisition function, a collation function, a judgment function, an output function, and a machine learning function. Each function overlaps with the description of welding judgment device 100 described above, and therefore details will be omitted.

[0067] 11 is a flow chart showing the flow of the welding judgment method of the welding judgment device of the embodiment, and includes various steps of a target information acquisition step (S110), a welding condition accumulation step (S120), and an appropriate information generation step (S130). In addition, the welding judgment method of the embodiment also includes various necessary steps (not shown) such as storage of calculation results, calling up the results, other calculations, input, output, storage, etc.

[0068] The object information acquisition function acquires welding object information related to the workpiece itself (S110; information acquisition step). The welding condition accumulation function acquires and accumulates, as accumulated information, a plurality of types of welding conditions controlled by the welding device when welding the workpiece in response to the welding object information over a predetermined period of time (S120; welding condition accumulation step). The suitability information generation function generates and accumulates, by a statistical method using the accumulated information, a connection between the welding conditions corresponding to the welding object information that is associated with the suitability of the welding quality as suitability information, as suitability information, using the accumulated information (S130; suitability information generation step).

[0069] 12 is a flow chart showing the continuation of the flow of the welding judgment method of the welding judgment device of the embodiment, and includes various steps of a sequential acquisition step (S140), a collation step (S150), a judgment step (S160), and an output step (S170). In addition, the welding judgment method of the embodiment also includes various necessary steps (not shown) such as storage of the calculation results, calling up the results, other calculations, input, output, storage, etc.

[0070] The sequential acquisition function acquires, as sequential welding condition information, welding conditions of the same type as the multiple types of welding conditions controlled by the welding device when welding the workpiece corresponding to the welding object information (S140; sequential acquisition step). The collation function collates the sequential welding condition information with the appropriate information (S150; collation step). The determination function determines whether the sequential welding condition information deviates from the appropriate information and generates a determination result (S160; determination step). The output function outputs the determination result (S170; output step).

[0071] 13 is a flow chart showing a continuation of the flow of the welding judgment method of the welding judgment device of the embodiment, and particularly shows a case where the machine learning unit 180 operates when performing machine learning in the suitability information generation 130. The suitability information generation step (S130) includes a machine learning step (S180).

[0072] The welding judgment program of the embodiment can be implemented using, for example, scripting languages ​​such as ActionScript, JavaScript (registered trademark), Python, and Ruby, and compiler languages ​​such as C, C++, C#, Objective-C, Swift (registered trademark), and Java (registered trademark). [Explanation of symbols]

[0073] 1 Welding Judgment System 2,3,4 Welding equipment 11 Resistance welded parts 12,13 Electrode section 14 Metal melting area 20 Welding condition measurement section 21 Voltage measuring instrument 22 Non-contact ammeter 100 Welding judgment device 101 CPU 102 ROM 103 RAM 104 Storage section 105 Input section 106 Output section 107 Display section 110 Target information acquisition unit 120 Welding Condition Storage Section 130 Appropriate Information Generation Department 140 Sequential acquisition part 150 Collation Unit 160 Judgment section 170 Output section 180 Machine Learning Department W1, W2 Workpieces

Claims

1. A welding judgment device for judging a welding state in a resistance welding machine that welds workpieces, comprising: The welding judgment device includes: an object information acquisition unit that acquires, as welding object information, a shape of the workpiece, a material of the workpiece, and a number of welding points related to the workpiece itself; an input section for inputting whether the welding quality is acceptable or not; a welding condition storage unit that acquires a plurality of types of welding conditions for each welding point when the plurality of types of welding conditions controlled by the resistance welding machine in welding the workpieces in response to the welding object information are defined as a current and a voltage, a current application time, a pressure applied to the workpieces, and a resistance value during operation of the resistance welding machine, and that stores, as accumulated information, information in which the acquired plurality of types of welding conditions are associated with the quality of the weld inputted via the input unit; an appropriateness information generating unit that generates and accumulates, as appropriateness information, associations between a plurality of types of welding conditions corresponding to the welding object information that are associated with the acceptability of the welding quality by a statistical method using the accumulated information; a sequential acquisition unit that sequentially acquires, as welding condition information, the same types of welding conditions as the plurality of types of welding conditions controlled by the resistance welding machine when welding the workpiece corresponding to the welding object information; A collation unit that compares the sequential welding condition information with the appropriate information; a determination unit that determines whether the sequential welding condition information deviates from the appropriate information and generates a determination result; and an output unit that outputs the determination result. A welding judgment device characterized by:

2. The welding judgment device according to claim 1 , wherein the statistical method is machine learning using teacher data in which the welding quality when the workpiece is welded is used as teacher data and the plurality of types of welding conditions are used as input data.

3. 2. The welding judgment device according to claim 1, wherein the statistical method is a multivariate analysis in which the welding quality is a response variable and the plurality of types of welding conditions are explanatory variables.

4. a display unit that displays a time series graph of the welding conditions corresponding to each of the plurality of types of welding conditions included in the suitability information; 2. The welding judgment device according to claim 1, wherein a predetermined fluctuation range is set for the time series graph.

5. a display unit that displays a time series graph of the welding conditions corresponding to each of the plurality of types of welding conditions included in the suitability information; The welding judgment device according to claim 1 , wherein a threshold value is set for a fluctuation range of the time series graph.

6. The welding judgment device according to claim 1 , wherein the judgment unit generates the judgment result by using a judgment formula obtained by a statistical method using the judgment result and the aptitude information.

7. The welding judgment device according to claim 1 , wherein the judgment unit judges whether or not at least one of the plurality of welding conditions included in the sequential welding condition information deviates from the appropriate information.

8. A welding judgment device as described in claim 1, wherein the resistance welding machines are multiple and the welding judgment device is implemented in each of the resistance welding machines.

9. A welding judgment system for judging a welding condition in a resistance welding machine that welds a workpiece, comprising: The welding judgment system includes: an object information acquisition unit that acquires, as welding object information, a shape of the workpiece, a material of the workpiece, and a number of welding points related to the workpiece itself; an input section for inputting whether the welding quality is acceptable or not; a welding condition storage unit that acquires a plurality of types of welding conditions for each welding point when the plurality of types of welding conditions controlled by the resistance welding machine in welding the workpieces in response to the welding object information are defined as a current and a voltage, a current application time, a pressure applied to the workpieces, and a resistance value during operation of the resistance welding machine, and that stores, as accumulated information, information in which the acquired plurality of types of welding conditions are associated with the quality of the weld inputted via the input unit; an appropriateness information generating unit that generates and accumulates, as appropriateness information, associations between a plurality of types of welding conditions corresponding to the welding object information that are associated with the acceptability of the welding quality by a statistical method using the accumulated information; a sequential acquisition unit that sequentially acquires, as welding condition information, the same types of welding conditions as the plurality of types of welding conditions controlled by the resistance welding machine when welding the workpiece corresponding to the welding object information; A collation unit that compares the sequential welding condition information with the appropriate information; a determination unit that determines whether the sequential welding condition information deviates from the appropriate information and generates a determination result; and an output unit that outputs the determination result. A welding judgment system comprising:

10. A welding judgment system as described in Claim 9, wherein the resistance welding machines are multiple.

11. A welding judgment method for a welding judgment device that judges a welding state in a resistance welding machine that welds a workpiece, comprising: The computer of the welding judgment device an object information acquisition step of acquiring welding object information relating to the workpiece itself, the shape of the workpiece, the material of the workpiece, and the number of welding points as welding object information; an input step in which the acceptability of the welding quality is input; a welding condition storage step of acquiring a plurality of types of welding conditions for each welding point, the plurality of types of welding conditions being controlled by the resistance welding machine when welding the workpieces in accordance with the welding object information, the plurality of types of welding conditions being a current and a voltage during operation of the resistance welding machine, a current application time, a welding pressure applied to the workpieces, and a resistance value, and storing information as accumulated information in which the acquired plurality of types of welding conditions are associated with the quality of the weld inputted through the input step; a suitability information generating step of generating and storing, as suitability information, a connection between a plurality of types of welding conditions corresponding to the welding object information, which is associated with the suitability of the welding quality, by a statistical method using the accumulated information; a sequential acquisition step of sequentially acquiring, as welding condition information, the same types of welding conditions as the plurality of types of welding conditions controlled by the resistance welding machine when welding the workpiece corresponding to the welding object information; A comparison step of comparing the sequential welding condition information with the appropriate information; a determination step of determining whether the sequential welding condition information deviates from the appropriate information and generating a determination result; and outputting the determination result. A welding judgment method comprising:

12. A welding judgment program for a welding judgment device for judging a welding state in a resistance welding machine that welds a workpiece, comprising: The welding judgment device's computer An object information acquisition function for acquiring welding object information related to the workpiece itself, the shape of the workpiece, the material of the workpiece, and the number of welding points as welding object information; An input function for inputting whether the welding quality is acceptable or not; a welding condition storage function that acquires a plurality of types of welding conditions for each welding point when the plurality of types of welding conditions controlled by the resistance welding machine in welding the workpieces in response to the welding object information are defined as a current and a voltage, a current application time, a welding pressure applied to the workpieces, and a resistance value during operation of the resistance welding machine, and that stores information associated with each of the acquired plurality of types of welding conditions and the acceptability of the welding quality input via the input function as accumulated information; a suitability information generating function that generates and accumulates a connection between the welding conditions corresponding to the welding object information, which is associated with the suitability of the welding quality, as suitability information by a statistical method using the accumulated information; a sequential acquisition function for sequentially acquiring, as welding condition information, the same types of welding conditions as the plurality of types of welding conditions controlled by the resistance welding machine when welding the workpiece corresponding to the welding object information; A collation function for collating the sequential welding condition information with the appropriate information; a determination function for determining whether the sequential welding condition information deviates from the appropriate information and generating a determination result; An output function for outputting the judgment result. A welding judgment program comprising:

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